Short‐run and long‐run dynamics of farm land allocation: panel data evidence from Denmark<sup>1</sup>
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This study develops and estimates a dynamic multi‐output model of farmers’ land allocation decisions that allows for the gradual adjustment of allocations that can result from crop rotation practices and quasi‐fixed capital constraints. Estimation is based on micro‐panel data from Danish farmers that include acreage, output, and variable input utilization at the crop level. Results indicate that there are substantial differences between the short‐run and long‐run land allocation behaviour of Danish farmers and that there are substantial differences in the time lags associated with different crops. To our knowledge, this is the first dynamic micro‐model of land allocation estimated on data from the temperate climate zone. Since similar farming conditions are found in northern Europe and parts of the United States and Canada, this result may be of wider interest.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it